ArticleSensors (Basel, Switzerland)2022
Generation of Individualized Synthetic Data for Augmentation of the Type 1 Diabetes Data Sets Using Deep Learning Models.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- On the road to fully automated insulin delivery: A systematic review of meal announcement free algorithms.PLOS digital health · 2026Article
- Crossing borders securely: synthetic data and federated networks for privacy-preserving access to real-world data and emerging use cases.NPJ digital medicine · 2025Review
- Generative artificial intelligence in diabetes healthcare.iScience · 2025Review
- Machine learning for grading prediction and survival analysis in high grade glioma.Scientific reports · 2025Article
- Synthetic Data in Healthcare and Drug Development: Definitions, Regulatory Frameworks, Issues.CPT: pharmacometrics & systems pharmacology · 2025Review
- Simulating Realistic Continuous Glucose Monitor Time Series By Data Augmentation.Journal of diabetes science and technology · 2025Article
- Validation of the UVA Simulation Replay Methodology Using Clinical Data: Reproducing a Randomized Clinical Trial.Diabetes technology & therapeutics · 2024Article
- Generative deep learning for the development of a type 1 diabetes simulator.Communications medicine · 2024Article
- Virtual Scenarios of Earthquake Early Warning to Disaster Management in Smart Cities Based on Auxiliary Classifier Generative Adversarial Networks.Sensors (Basel, Switzerland) · 2023Article
- Disability risk prediction model based on machine learning among Chinese healthy older adults: results from the China Health and Retirement Longitudinal Study.Frontiers in public health · 2023Article
- FGL-GAN: Global-Local Mask Generative Adversarial Network for Flame Image Composition.Sensors (Basel, Switzerland) · 2022Article
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Authors and funding
5 authors.
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Abstract
In this paper, we present a methodology based on generative adversarial network architecture to generate synthetic data sets with the intention of augmenting continuous glucose monitor data from individual patients. We use these synthetic data with the aim of improving the overall performance of prediction models based on machine learning techniques. Experiments were performed on two cohorts of patients suffering from type 1 diabetes mellitus with significant differences in their clinical outcomes. In the first contribution, we have demonstrated that the chosen methodology is able to replicate the intrinsic characteristics of individual patients following the statistical distributions of the original data. Next, a second contribution demonstrates the potential of synthetic data to improve the performance of machine learning approaches by testing and comparing different prediction models for the problem of predicting nocturnal hypoglycemic events in type 1 diabetic patients. The results obtained for both generative and predictive models are quite encouraging and set a precedent in the use of generative techniques to train new machine learning models.
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